From outside to hyper-globalisation: an Artificial Neural Network ordinal classifier applied to measure the extent of globalisation

Globalisation has become a key concept in the social sciences to understand the accelerating changes occurred in modern societies during recent decades. As a consequence, measuring the influence of globalisation on the economic, social and political aspects of nations has been a requirement. There a...

ver descrição completa

Detalhes bibliográficos
Autores: Dorado Moreno, Manuel, Sianes Castaño, Antonio Manuel, Hervás Martínez, César
Formato: artículo
Fecha de publicación:2016
País:España
Recursos:Universidad Loyola Andalucía
Repositorio:Brújula
OAI Identifier:oai:repositorio.uloyola.es:20.500.12412/1208
Acesso em linha:http://hdl.handle.net/20.500.12412/1208
Access Level:acceso abierto
Palavra-chave:Ranking
Ordinal classification
Artificial Neural Networks
Globalisation
Indices of Globalisation
id ES_7e2fd9b28d0d32e4e0ff9520f9158338
oai_identifier_str oai:repositorio.uloyola.es:20.500.12412/1208
network_acronym_str ES
network_name_str España
repository_id_str
spelling From outside to hyper-globalisation: an Artificial Neural Network ordinal classifier applied to measure the extent of globalisationDorado Moreno, ManuelSianes Castaño, Antonio ManuelHervás Martínez, CésarRankingOrdinal classificationArtificial Neural NetworksGlobalisationIndices of GlobalisationGlobalisation has become a key concept in the social sciences to understand the accelerating changes occurred in modern societies during recent decades. As a consequence, measuring the influence of globalisation on the economic, social and political aspects of nations has been a requirement. There are many indices at present to calculate the extent of globalisation reached by each country. However, most of the methods used to build those indices suffer certain methodological limitations that hinder the wider dissemination and usefulness of their results. As an alternative, in this paper, we propose a methodology for ordinal ranking of countries associated with their globalisation level, which gives us an easier and more useful information about the different levels where countries are regarding to this criteria. Among Computational Intelligence techniques, Artificial Neural Networks (ANNs)havebecomedominantmodellingparadigm.Wehavebuiltanovelnon-linearordinal classifier by combining the Proportional Odd Models (POM) with ANNs that is able to classify countries according to their level of globalisation in six classes, which range from hyperglobalised countries to countries that remain outside the process of globalisation. The results could not be more encouraging. Our experiments yield robust results and show better outcomesthanalternativelinearandnon-linearordinalclassifiers,whichraisesthepossibility of developing a model of classification that might overcome some of the limitations of the indices currently employed to measure globalisation.2016info:eu-repo/semantics/articlehttp://hdl.handle.net/20.500.12412/1208reponame:Brújulainstname:Universidad Loyola AndalucíaIngléshttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.uloyola.es:20.500.12412/12082026-06-24T12:48:37Z
dc.title.none.fl_str_mv From outside to hyper-globalisation: an Artificial Neural Network ordinal classifier applied to measure the extent of globalisation
title From outside to hyper-globalisation: an Artificial Neural Network ordinal classifier applied to measure the extent of globalisation
spellingShingle From outside to hyper-globalisation: an Artificial Neural Network ordinal classifier applied to measure the extent of globalisation
Dorado Moreno, Manuel
Ranking
Ordinal classification
Artificial Neural Networks
Globalisation
Indices of Globalisation
title_short From outside to hyper-globalisation: an Artificial Neural Network ordinal classifier applied to measure the extent of globalisation
title_full From outside to hyper-globalisation: an Artificial Neural Network ordinal classifier applied to measure the extent of globalisation
title_fullStr From outside to hyper-globalisation: an Artificial Neural Network ordinal classifier applied to measure the extent of globalisation
title_full_unstemmed From outside to hyper-globalisation: an Artificial Neural Network ordinal classifier applied to measure the extent of globalisation
title_sort From outside to hyper-globalisation: an Artificial Neural Network ordinal classifier applied to measure the extent of globalisation
dc.creator.none.fl_str_mv Dorado Moreno, Manuel
Sianes Castaño, Antonio Manuel
Hervás Martínez, César
author Dorado Moreno, Manuel
author_facet Dorado Moreno, Manuel
Sianes Castaño, Antonio Manuel
Hervás Martínez, César
author_role author
author2 Sianes Castaño, Antonio Manuel
Hervás Martínez, César
author2_role author
author
dc.subject.none.fl_str_mv Ranking
Ordinal classification
Artificial Neural Networks
Globalisation
Indices of Globalisation
topic Ranking
Ordinal classification
Artificial Neural Networks
Globalisation
Indices of Globalisation
description Globalisation has become a key concept in the social sciences to understand the accelerating changes occurred in modern societies during recent decades. As a consequence, measuring the influence of globalisation on the economic, social and political aspects of nations has been a requirement. There are many indices at present to calculate the extent of globalisation reached by each country. However, most of the methods used to build those indices suffer certain methodological limitations that hinder the wider dissemination and usefulness of their results. As an alternative, in this paper, we propose a methodology for ordinal ranking of countries associated with their globalisation level, which gives us an easier and more useful information about the different levels where countries are regarding to this criteria. Among Computational Intelligence techniques, Artificial Neural Networks (ANNs)havebecomedominantmodellingparadigm.Wehavebuiltanovelnon-linearordinal classifier by combining the Proportional Odd Models (POM) with ANNs that is able to classify countries according to their level of globalisation in six classes, which range from hyperglobalised countries to countries that remain outside the process of globalisation. The results could not be more encouraging. Our experiments yield robust results and show better outcomesthanalternativelinearandnon-linearordinalclassifiers,whichraisesthepossibility of developing a model of classification that might overcome some of the limitations of the indices currently employed to measure globalisation.
publishDate 2016
dc.date.none.fl_str_mv 2016
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12412/1208
url http://hdl.handle.net/20.500.12412/1208
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Brújula
instname:Universidad Loyola Andalucía
instname_str Universidad Loyola Andalucía
reponame_str Brújula
collection Brújula
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869411718180896768
score 15,811543

Registos relacionados